Robert Morris University

Abstract Credibility theory provides a fundamental framework in actuarial science for estimating policyholder premiums by blending individual claims experience with overall portfolio data. Bühlmann and Bühlmann–Straub credibility models are widely used because, in the Bayesian hierarchical setting, they are the best linear Bayes estimators, minimizing the Bayes risk (expected squared error loss) within the class of linear estimators given the experience data for a particular risk class. To improve estimation accuracy, quadratic credibility models incorporate higher-order terms, capturing more information about the underlying risk structure. This study develops a robust quadratic credibility (RQC) framework that integrates second-order polynomial adjustments of robustly transformed ground-up loss data, such as winsorized moments, to improve stability in the presence of extreme claims or heavy-tailed distributions. Extending semi-linear credibility, RQC maintains interpretability while enhancing statistical efficiency. We establish its asymptotic properties, derive closed-form expressions for the RQC premium, and demonstrate its superior performance in reducing mean square error (MSE). We additionally derive semi-linear credibility structural parameters using winsorized data, further strengthening the robustness of credibility estimation. Analytical comparisons and empirical applications highlight RQC’s ability to capture claim heterogeneity, offering a more reliable and equitable approach to premium estimation. This research advances credibility theory by introducing a refined methodology that balances efficiency, robustness, and practical applicability across diverse insurance settings.

In this paper, we present the application of Entrepreneurial Separation to Transfer Technologies from federally funded research and the difficulties associated with the transfer of intellectual property. With the increased threat (call) by the government to exercise “march-in” rights, which could limit both the licensing terms and what firms charge for goods (e.g., prescription drugs) that come from intellectual property (IP) resulting from federally funded research, researchers may be disinclined to commercialize their IP. While the government wants to exercise its March-In Rights to help consumers, it may be unintentionally harming them. The government is increasingly more vocal about the threat of march-in rights, in part because of the high consumer prices that have resulted from pandemic-related inflationary pressures. This threat has the potential of rolling back 40 years of gains from the Bayh–Dole Act. We present an overview of Entrepreneurial Separation to Transfer Technology and Entrepreneurial Leave Agreements and how they serve as one tool to support the transfer of early-stage technology. In a Volatile, Uncertain, Complex, and Ambiguous environment, university and federal laboratories need all the tools available to facilitate innovation and its commercialization. We present here why the development of these programs can help support their activities.

Threat detection and response mechanisms in organizations have been revolutionized by the integration of Artificial Intelligence (AI) into cybersecurity frameworks. The ability to predict and identify potential breaches and anomalies and automate responses has been enhanced by machine learning (ML) algorithms, thereby fortifying digital infrastructures against evolving threats. However, new challenges are introduced by this advancement, as adversaries exploit AI to serve their negative interest by developing sophisticated attack vectors, including adaptive malware and deepfakes. This paper delves into the dual nature of AI in cybersecurity, analyzing its role as both a defender and a potential threat. Likewise, it delves into current applications such as threat intelligence, behavioral analytics, and anomaly detection. It also examines and addresses the emerging challenges, including data privacy concerns, adversarial AI, and the ethical implications of automated decision-making in security contexts. This paper discusses the emergence of adversarial AI, and high spots the importance of Explainable AI (XAI) in maintaining transparency and trust in automated security systems. By this paper, not only will the cybersecurity community gain insights into developing robust AI-driven security strategies that anticipate and mitigate the risks posed by malicious AI applications but also gain insights into the latest research findings, case studies of AI-driven cybersecurity implementations, and best practices for integrating AI technologies into existing security infrastructures.

As generative artificial intelligence systems such as ChatGPT, Gemini, and Midjourney become integral to the digital lives of Generation Z, understanding users' privacy literacy is essential for improving AI platform transparency and usability. This study proposes the DCPS (Declarative, Cognitive and Procedural Score) framework to assess AI privacy literacy. The DCPS framework measures privacy literacy in three dimensions: Declarative Knowledge (factual understanding of data practices), Cognitive Knowledge (evaluation of privacy risk, control, and trust), and Procedural Knowledge (practical skills in managing privacy settings) and establishes a 0-10 composite score indicating total privacy literacy. A pilot study of 73 university students provided an empirical baseline for Generation Z's AI privacy literacy. Results show low declarative knowledge and cognitive awareness of privacy risks, but very low procedural competence, revealing a substantial gap between awareness and action. The DCPS privacy literacy framework in AI contexts provides valuable guidance to help improving transparency, user education, and privacy-centered design in generative AI systems.

Prior research has been conducted regarding face-to-face, online, and blended/hybrid teaching methods for courses in higher education. This study adds to the deficient prior research in accounting courses by leveraging undergraduate student feedback from the face-to-face and online elements of an experimental blended/hybrid environment in Intermediate Accounting to build an enhanced future blended/hybrid model. This course followed the face-to-face teaching method for half of the semester and changed to an online environment for the second half of the semester. The study's results are based on student feedback from a survey and focus group guided by the research questions on the satisfaction/dissatisfaction and advantages/disadvantages of the experimental blended model. The goal of this research is to analyze and leverage student perspectives to develop key insights into how face-to-face and online components can be strategically integrated into an innovative future blended/hybrid model in Intermediate Accounting to enhance student satisfaction, engagement, and learning outcomes.

This study explores factors influencing teacher self-efficacy among preservice teachers and principal candidates. Through qualitative focus groups, findings highlight the significance of relationship building, social-emotional learning, and support systems in enhancing preservice teacher confidence, emphasizing the principal’s role in fostering a supportive educational environment.

The flexibility of the unstructured supplementary service data (USSD) across mobile phones has caused its adoption surge as a payment channel. Its usage accommodates financial inclusivity and extends customer reach irrespective of their specific phone capabilities. With data conveyed on the USSD channel in plaintext–this has raised vulnerability issues with shoulder surfing attacks. The use of password yielded extra layer of security as authentication to USSD-based services. But, the rise in password guess attacks has necessitated a new scheme. This study is a randomized-obfuscated single factor authentication (SFA) mode via a 5-digit PIN-entry as requisite for the USSD channel. It yields a list via which users select a key-array that corresponds to their PIN as concealed in a 10-digit array. Expert assess of MoBiSafe’s usability and security against shoulder-surf yielded 10.1 msecs and 2.26 msecs respectively to outperform existing models that utilize direct/indirect PIN-entry as in USSD transactions. And this was found to be both secure, usable and acceptable.